OpenRaiser/Intern-Atlas huggingface dataset:
Towards Fair and Efficient Public Transportation: A Bus Stop Model
Venue: arXiv cs.GT 2025
Year: 2025

We consider a stylized formal model of public transportation, where a set of agents need to travel along a given road, and there is a bus that runs the length of this road. Each agent has a left terminal and a right terminal between which they wish to travel; they can walk all the way, or walk to/from the nearest stop and use the bus for the rest of their journey. The bus can make a fixed number of stops, and the planner needs to select locations for these stops. We study notions of efficiency and fairness for this setting. First, we give a polynomial-time algorithm for computing a solution that minimizes the total travel time; our approach can capture further extensions of the base model, such as more general cost functions or existing infrastructure. Second, we develop a polynomial-time algorithm that outputs solutions with provable fairness guarantees (such as a variant of the justified representation axiom or $2$-approximate core) as long as the agents' costs only depend on the distance they need to walk. Our simulations indicate that our algorithm almost always outputs fair solutions, even for parameter regimes that do not admit theoretical guarantees.
Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI
Venue: arXiv cs.CY 2025
Year: 2025

AI-based systems, including Large Language Models (LLM), impact millions by supporting diverse tasks but face issues like misinformation, bias, and misuse. AI ethics is crucial as new technologies and concerns emerge, but objective, practical guidance remains debated. This study examines the use of LLMs for AI ethics in practice, assessing how LLM trustworthiness-enhancing techniques affect software development in this context. Using the Design Science Research (DSR) method, we identify techniques for LLM trustworthiness: multi-agents, distinct roles, structured communication, and multiple rounds of debate. We design a multi-agent prototype LLM-MAS, where agents engage in structured discussions on real-world AI ethics issues from the AI Incident Database. We evaluate the prototype across three case scenarios using thematic analysis, hierarchical clustering, comparative (baseline) studies, and running source code. The system generates approximately 2,000 lines of code per case, compared to only 80 lines in baseline trials. Discussions reveal terms like bias detection, transparency, accountability, user consent, GDPR compliance, fairness evaluation, and EU AI Act compliance, showing this prototype ability to generate extensive source code and documentation addressing often overlooked AI ethics issues. However, practical challenges in source code integration and dependency management may limit its use by practitioners.
A Learning-Based Control Barrier Function for Car-Like Robots: Toward Less Conservative Collision Avoidance
Venue: arXiv cs.RO 2025
Year: 2025

We propose a learning-based Control Barrier Function (CBF) to reduce conservatism in collision avoidance for car-like robots. Traditional CBFs often use the Euclidean distance between robots' centers as a safety margin, which neglects their headings and approximates their geometries as circles. Although this simplification meets the smoothness and differentiability requirements of CBFs, it may result in overly conservative behavior in dense environments. We address this by designing a safety margin that considers both the robot's heading and actual shape, thereby enabling a more precise estimation of safe regions. Because this safety margin is non-differentiable, we approximate it with a neural network to ensure differentiability. In addition, we propose a notion of relative dynamics that makes the learning process tractable. In a case study, we establish the theoretical foundation for applying this notion to a nonlinear kinematic bicycle model. Numerical experiments in overtaking and bypassing scenarios show that our approach reduces conservatism (e.g., requiring 33.5% less lateral space for bypassing) without incurring significant extra computation time. Code: https://github.com/bassamlab/sigmarl
VideoCogQA: A Controllable Benchmark for Evaluating Cognitive Abilities in Video-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in Large Video-Language Models (LVLMs) have led to promising results in multimodal video understanding. However, it remains unclear whether these models possess the cognitive capabilities required for high-level tasks, particularly those involving symbolic and abstract perception. Existing benchmarks typically rely on real-world, annotated videos, which lack control over video content and inherent difficulty, limiting their diagnostic power. To bridge this gap, we propose VideoCogQA, a scalable and fully controllable benchmark inspired by game-world environments, designed to evaluate the cognitive abilities of LVLMs. By generating synthetic videos via a programmatic engine, VideoCogQA allows fine-grained control over visual elements, temporal dynamics, and task difficulty. This approach enables a focused evaluation of video cognitive abilities, independent of prior knowledge from visual scene semantics. The dataset includes 800 videos and 3,280 question-answer pairs, featuring tasks related to abstract concepts, symbolic elements, and multimodal integration, with varying levels of difficulty. Experimental results show that even state-of-the-art (SOTA) models, such as GPT-4o, achieve an average performance of 48.8% on tasks involving abstract concepts. Additionally, performance drops by 15% as task complexity increases, highlighting the challenges LVLMs face in maintaining consistent performance. Through this work, we hope to show the limitations of current LVLMs and offer insights into how they can more effectively emulate human cognitive processes in the future.
Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide
Venue: arXiv cs.CL 2025
Year: 2025

Fine-tuning large language models (LLMs) with limited data poses a practical challenge in low-resource languages, specialized domains, and constrained deployment settings. While pre-trained LLMs provide strong foundations, effective adaptation under data scarcity requires focused and efficient fine-tuning techniques. This paper presents a structured and practical survey of recent methods for fine-tuning LLMs in data-scarce scenarios. We systematically review parameter-efficient fine-tuning techniques that lower training and deployment costs, domain and cross-lingual adaptation methods for both encoder and decoder models, and model specialization strategies. We further examine preference alignment approaches that guide model behavior using limited human or synthetic feedback, emphasizing sample and compute efficiency. Throughout, we highlight empirical trade-offs, selection criteria, and best practices for choosing suitable techniques based on task constraints, including model scaling, data scaling, and the mitigation of catastrophic forgetting. The aim is to equip researchers and practitioners with actionable insights for effectively fine-tuning LLMs when data and resources are limited.
BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency
Venue: arXiv cs.CL 2025
Year: 2025

While current large language models have achieved a remarkable success, their data efficiency remains a challenge to overcome. Recently it has been suggested that child-directed speech (CDS) can improve training data efficiency of modern language models based on Transformer neural networks. However, it is not yet understood which specific properties of CDS are effective for training these models. In the context of the BabyLM Challenge, we focus on Variation Sets (VSs), sets of consecutive utterances expressing a similar intent with slightly different words and structures, which are ubiquitous in CDS. To assess the impact of VSs on training data efficiency, we augment CDS data with different proportions of artificial VSs and use these datasets to train an auto-regressive model, GPT-2. We find that the best proportion of VSs depends on the evaluation benchmark: BLiMP and GLUE scores benefit from the presence of VSs, but EWOK scores do not. Additionally, the results vary depending on multiple factors such as the number of epochs and the order of utterance presentation. Taken together, these findings suggest that VSs can have a beneficial influence on language models, while leaving room for further investigation.
NeuralDEM -- Real-time Simulation of Industrial Particulate Flows
Venue: arXiv cs.LG 2025
Year: 2025

Advancements in computing power have made it possible to numerically simulate large-scale fluid-mechanical and/or particulate systems, many of which are integral to core industrial processes. Among the different numerical methods available, the discrete element method (DEM) provides one of the most accurate representations of a wide range of physical systems involving granular and discontinuous materials. Consequently, DEM has become a widely accepted approach for tackling engineering problems connected to granular flows and powder mechanics. Additionally, DEM can be integrated with grid-based computational fluid dynamics (CFD) methods, enabling the simulation of chemical processes taking place, e.g., in fluidized beds. However, DEM is computationally intensive because of the intrinsic multiscale nature of particulate systems, restricting simulation duration or number of particles. Towards this end, NeuralDEM presents an end-to-end approach to replace slow numerical DEM routines with fast, adaptable deep learning surrogates. NeuralDEM is capable of picturing long-term transport processes across different regimes using macroscopic observables without any reference to microscopic model parameters. First, NeuralDEM treats the Lagrangian discretization of DEM as an underlying continuous field, while simultaneously modeling macroscopic behavior directly as additional auxiliary fields. Second, NeuralDEM introduces multi-branch neural operators scalable to real-time modeling of industrially-sized scenarios - from slow and pseudo-steady to fast and transient. Such scenarios have previously posed insurmountable challenges for deep learning models. Notably, NeuralDEM faithfully models coupled CFD-DEM fluidized bed reactors of 160k CFD cells and 500k DEM particles for trajectories of 28s. NeuralDEM will open many new doors to advanced engineering and much faster process cycles.
Probing LLM Hallucination from Within: Perturbation-Driven Approach via Internal Knowledge
Venue: arXiv cs.AI 2025
Year: 2025

LLM hallucination, where unfaithful text is generated, presents a critical challenge for LLMs' practical applications. Current detection methods often resort to external knowledge, LLM fine-tuning, or supervised training with large hallucination-labeled datasets. Moreover, these approaches do not distinguish between different types of hallucinations, which is crucial for enhancing detection performance. To address such limitations, we introduce hallucination probing, a new task that classifies LLM-generated text into three categories: aligned, misaligned, and fabricated. Driven by our novel discovery that perturbing key entities in prompts affects LLM's generation of these three types of text differently, we propose SHINE, a novel hallucination probing method that does not require external knowledge, supervised training, or LLM fine-tuning. SHINE is effective in hallucination probing across three modern LLMs, and achieves state-of-the-art performance in hallucination detection, outperforming seven competing methods across four datasets and four LLMs, underscoring the importance of probing for accurate detection.
Robustness Assessment of Assemblies in Frictional Contact
Venue: arXiv cs.RO 2025
Year: 2025

This work establishes a solution to the problem of assessing the capacity of multi-object assemblies to withstand external forces without becoming unstable. Our physically-grounded approach handles arbitrary structures made from rigid objects of any shape and mass distribution without relying on heuristics or approximations. The result is a method that provides a foundation for autonomous robot decision-making when interacting with objects in frictional contact. Our strategy relies on a contact interface graph representation to reason about instabilities and makes use of object shape information to decouple sub-problems and improve efficiency. Our algorithm can be used by motion planners to produce safe assembly transportation plans, and by object placement planners to select better poses. Compared to prior work, our approach is more generally applicable than commonly used heuristics and more efficient than dynamics simulations.
Learning Parameter Sharing with Tensor Decompositions and Sparsity
Venue: arXiv cs.LG 2025
Year: 2025

Large neural networks exhibit exceptional performance across numerous tasks, yet their considerable size often hinders deployment on resource-constrained systems. While various model compression strategies have been well studied, parameter sharing remains underexplored. In this paper, we introduce Fine-grained Parameter Sharing (FiPS), a novel algorithm that leverages parameter sharing, tensor decomposition, and sparsity to effectively compress large-scale Vision Transformers (ViTs) and Large Language Models (LLMs). FiPS employs a shared base and sparse factors to represent neurons across multi-layer perceptron (MLP) modules, where initialization is guided by Singular Value Decomposition (SVD) and subsequent optimization is conducted through block-wise reconstruction error minimization. Experimental results show that FiPS reduces the parameter budget of MLP modules by 50-75% for DeiT-B and Swin-L and by 40-50% for various Gemma-2 and Llama-3 models while maintaining ViT model accuracy within 1% pt. of the original and LLM perplexity with negligible degradation.
Advancing Stroke Risk Prediction Using a Multi-modal Foundation Model
Venue: arXiv cs.CV 2025
Year: 2025

Predicting stroke risk is a complex challenge that can be enhanced by integrating diverse clinically available data modalities. This study introduces a self-supervised multimodal framework that combines 3D brain imaging, clinical data, and image-derived features to improve stroke risk prediction prior to onset. By leveraging large unannotated clinical datasets, the framework captures complementary and synergistic information across image and tabular data modalities. Our approach is based on a contrastive learning framework that couples contrastive language-image pretraining with an image-tabular matching module, to better align multimodal data representations in a shared latent space. The model is trained on the UK Biobank, which includes structural brain MRI and clinical data. We benchmark its performance against state-of-the-art unimodal and multimodal methods using tabular, image, and image-tabular combinations under diverse frozen and trainable model settings. The proposed model outperformed self-supervised tabular (image) methods by 2.6% (2.6%) in ROC-AUC and by 3.3% (5.6%) in balanced accuracy. Additionally, it showed a 7.6% increase in balanced accuracy compared to the best multimodal supervised model. Through interpretable tools, our approach demonstrated better integration of tabular and image data, providing richer and more aligned embeddings. Gradient-weighted Class Activation Mapping heatmaps further revealed activated brain regions commonly associated in the literature with brain aging, stroke risk, and clinical outcomes. This robust self-supervised multimodal framework surpasses state-of-the-art methods for stroke risk prediction and offers a strong foundation for future studies integrating diverse data modalities to advance clinical predictive modelling.
SoK: Consensus for Fair Message Ordering
Venue: arXiv cs.DC 2025
Year: 2025

Distributed ledger systems, such as blockchains, rely on consensus protocols that commit ordered messages for processing. In practice, message ordering within these systems is often reward-driven. This raises concerns about fairness, particularly in decentralized finance applications, where nodes can exploit transaction orders to maximize rewards referred to as Maximal Extractable Value. This paper provides a systematic understanding of consensus protocols that order messages with different approaches, especially focusing on the ones that promote order fairness, using methods including First-In-First-Out (FIFO), random, and blind ordering. We review the challenges and trade-offs of deriving fair message ordering in a Byzantine fault-tolerant setting, and summarize the requirements for making a fair message ordering consensus protocol. We introduce a design guideline, with which we propose a latency optimization to the state-of-the-art FIFO ordering protocol of Themis. This work provides a systematic way for assessing and enhancing message order fairness in blockchain systems.
Some Thoughts on Graph Similarity
Venue: arXiv cs.DM 2025
Year: 2025

We give an overview of different approaches to measuring the similarity of, or the distance between, two graphs, highlighting connections between these approaches. We also discuss the complexity of computing the distances.
Uncertainty in Supply Chain Digital Twins: A Quantum-Classical Hybrid Approach
Venue: arXiv cs.LG 2025
Year: 2025

This study investigates uncertainty quantification (UQ) using quantum-classical hybrid machine learning (ML) models for applications in complex and dynamic fields, such as attaining resiliency in supply chain digital twins and financial risk assessment. Although quantum feature transformations have been integrated into ML models for complex data tasks, a gap exists in determining their impact on UQ within their hybrid architectures (quantum-classical approach). This work applies existing UQ techniques for different models within a hybrid framework, examining how quantum feature transformation affects uncertainty propagation. Increasing qubits from 4 to 16 shows varied model responsiveness to outlier detection (OD) samples, which is a critical factor for resilient decision-making in dynamic environments. This work shows how quantum computing techniques can transform data features for UQ, particularly when combined with classical methods.
MDHP-Net: Detecting an Emerging Time-exciting Threat in IVN
Venue: arXiv cs.CR 2025
Year: 2025

The integration of intelligent and connected technologies in modern vehicles, while offering enhanced functionalities through Electronic Control Unit (ECU) and interfaces like OBD-II and telematics, also exposes the vehicle's in-vehicle network (IVN) to potential cyberattacks. Unlike prior work, we identify a new time-exciting threat model against IVN. These attacks inject malicious messages that exhibit a time-exciting effect, gradually manipulating network traffic to disrupt vehicle operations and compromise safety-critical functions. We systematically analyze the characteristics of the threat: dynamism, time-exciting impact, and low prior knowledge dependency. To validate its practicality, we replicate the attack on a real Advanced Driver Assistance System via Controller Area Network (CAN), exploiting Unified Diagnostic Service vulnerabilities and proposing four attack strategies. While CAN's integrity checks mitigate attacks, Ethernet migration (e.g., DoIP/SOME/IP) introduces new surfaces. We further investigate the feasibility of time-exciting threat under SOME/IP. To detect time-exciting threat, we introduce MDHP-Net, leveraging Multi-Dimentional Hawkes Process (MDHP) and temporal and message-wise feature extracting structures. Meanwhile, to estimate MDHP parameters, we developed the first GPU-optimized gradient descent solver for MDHP (MDHP-GDS). These modules significantly improves the detection rate under time-exciting attacks in multi-ECU IVN system. To address data scarcity, we release STEIA9, the first open-source dataset for time-exciting attacks, covering 9 Ethernet-based attack scenarios. Extensive experiments on STEIA9 (9 attack scenarios) show MDHP-Net outperforms 3 baselines, confirming attack feasibility and detection efficacy.
Systolic Arrays and Structured Pruning Co-design for Efficient Transformers in Edge Systems
Venue: arXiv cs.AR 2025
Year: 2025

Efficient deployment of resource-intensive transformers on edge devices necessitates cross-stack optimization. We thus study the interrelation between structured pruning and systolic acceleration, matching the size of pruned blocks with the systolic array dimensions. In this setting, computations of pruned weight blocks can be skipped, reducing run-time and energy consumption, but potentially impacting quality of service (QoS). To evaluate the trade-offs between systolic array size and sparsity opportunities, we present a novel co-design framework that integrates algorithmic optimization, system simulation, and hardware design. Targeting speech recognition and machine translation using transformers as case study, we analyze how configuration choices across the stack affect performance metrics. Results demonstrate that structured pruning on systems featuring systolic array acceleration can effectively increase performance, while maintaining high QoS levels. Up to 44% system-wide speedups due to structured pruning and quantization were measured, with only 1.4% word error rate degradation on the standard LibriSpeech dataset.
Modification Takes Courage: Seamless Image Stitching via Reference-Driven Inpainting
Venue: arXiv cs.CV 2025
Year: 2025

Current image stitching methods often produce noticeable seams in challenging scenarios such as uneven hue and large parallax. To tackle this problem, we propose the Reference-Driven Inpainting Stitcher (RDIStitcher), which reformulates the image fusion and rectangling as a reference-based inpainting model, incorporating a larger modification fusion area and stronger modification intensity than previous methods. Furthermore, we introduce a self-supervised model training method, which enables the implementation of RDIStitcher without requiring labeled data by fine-tuning a Text-to-Image (T2I) diffusion model. Recognizing difficulties in assessing the quality of stitched images, we present the Multimodal Large Language Models (MLLMs)-based metrics, offering a new perspective on evaluating stitched image quality. Compared to the state-of-the-art (SOTA) method, extensive experiments demonstrate that our method significantly enhances content coherence and seamless transitions in the stitched images. Especially in the zero-shot experiments, our method exhibits strong generalization capabilities. Code: https://github.com/yayoyo66/RDIStitcher
M3TR: A Generalist Model for Real-World HD Map Completion
Venue: arXiv cs.CV 2025
Year: 2025

Autonomous vehicles rely on HD maps for their operation, but offline HD maps eventually become outdated. For this reason, online HD map construction methods use live sensor data to infer map information instead. Research on real map changes shows that oftentimes entire parts of an HD map remain unchanged and can be used as a prior. We therefore introduce M3TR (Multi-Masking Map Transformer), a generalist approach for HD map completion both with and without offline HD map priors. As a necessary foundation, we address shortcomings in ground truth labels for Argoverse 2 and nuScenes and propose the first comprehensive benchmark for HD map completion. Unlike existing models that specialize in a single kind of map change, which is unrealistic for deployment, our Generalist model handles all kinds of changes, matching the effectiveness of Expert models. With our map masking as augmentation regime, we can even achieve a +1.4 mAP improvement without a prior. Finally, by fully utilizing prior HD map elements and optimizing query designs, M3TR outperforms existing methods by +4.3 mAP while being the first real-world deployable model for offline HD map priors. Code is available at https://github.com/immel-f/m3tr